{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-04-11T12:46:53.846372Z",
     "start_time": "2025-04-11T12:46:47.573830Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "import time\n",
    "import pandas as pd\n",
    "\n",
    "# 加载MNIST数据集\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist.data, mnist.target.astype(np.int8)\n",
    "\n",
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T12:47:37.479906Z",
     "start_time": "2025-04-11T12:47:29.778407Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 初始化感知机模型\n",
    "perceptron = Perceptron(random_state=42)\n",
    "\n",
    "# 记录训练时间\n",
    "start_time = time.time()\n",
    "perceptron.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = perceptron.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='weighted')\n",
    "recall = recall_score(y_test, y_pred, average='weighted')\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Perceptron - Accuracy:\", accuracy)\n",
    "print(\"Perceptron - Precision:\", precision)\n",
    "print(\"Perceptron - Recall:\", recall)\n",
    "print(\"Perceptron - F1 Score:\", f1)\n",
    "print(\"Perceptron - Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"Perceptron - Training Time:\", train_time, \"seconds\")"
   ],
   "id": "409cae08ad96e490",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perceptron - Accuracy: 0.8810714285714286\n",
      "Perceptron - Precision: 0.8809648832560745\n",
      "Perceptron - Recall: 0.8810714285714286\n",
      "Perceptron - F1 Score: 0.880775650387065\n",
      "Perceptron - Confusion Matrix:\n",
      " [[1276    2   21    7    1    7   17    4    8    0]\n",
      " [   1 1503    6   10    4   13    1   29   31    2]\n",
      " [   7   15 1231   27   15   17   14   14   33    7]\n",
      " [   9   16   34 1221    4   33   12   35   42   27]\n",
      " [   3    3   14    6 1160    6   12   10   24   57]\n",
      " [  10   13   15   59   21 1034   29    6   75   11]\n",
      " [   4   20   52    3   10   11 1277    2   14    3]\n",
      " [   8    4   15    6   13   16    0 1394    6   41]\n",
      " [  11   42   21   57    8   70   12   13 1089   34]\n",
      " [   8    3    4   24   72   26    0  101   32 1150]]\n",
      "Perceptron - Training Time: 7.651254177093506 seconds\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T12:47:40.348431Z",
     "start_time": "2025-04-11T12:47:40.032254Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='YlGnBu',\n",
    "            xticklabels=perceptron.classes_, yticklabels=perceptron.classes_)\n",
    "plt.title('Perceptron Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ],
   "id": "661397c3277710c8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T12:48:46.319294Z",
     "start_time": "2025-04-11T12:48:03.271423Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# 初始化 MLP 模型\n",
    "mlp = MLPClassifier(hidden_layer_sizes=(128, 64), max_iter=100, random_state=42)\n",
    "\n",
    "# 记录训练时间\n",
    "start_time = time.time()\n",
    "mlp.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = mlp.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='weighted')\n",
    "recall = recall_score(y_test, y_pred, average='weighted')\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"MLP - Accuracy:\", accuracy)\n",
    "print(\"MLP - Precision:\", precision)\n",
    "print(\"MLP - Recall:\", recall)\n",
    "print(\"MLP - F1 Score:\", f1)\n",
    "print(\"MLP - Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"MLP - Training Time:\", train_time, \"seconds\")"
   ],
   "id": "dd282f390f2f2194",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP - Accuracy: 0.9761428571428571\n",
      "MLP - Precision: 0.9761329733059578\n",
      "MLP - Recall: 0.9761428571428571\n",
      "MLP - F1 Score: 0.9761288865810465\n",
      "MLP - Confusion Matrix:\n",
      " [[1325    0    4    0    0    0    7    1    6    0]\n",
      " [   0 1588    2    2    1    0    0    5    2    0]\n",
      " [   0    6 1342    5    4    1    5    6   10    1]\n",
      " [   0    0   12 1391    2   13    0   10    2    3]\n",
      " [   2    1    3    1 1260    1    3    4    1   19]\n",
      " [   0    0    0   13    3 1238    9    2    8    0]\n",
      " [   3    1    0    0    6    4 1381    0    1    0]\n",
      " [   2    3   10    1    2    2    0 1469    2   12]\n",
      " [   3    3   12   10    3    6    4    5 1300   11]\n",
      " [   7    3    0    6   14    2    0   10    6 1372]]\n",
      "MLP - Training Time: 42.948880195617676 seconds\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T12:48:50.616050Z",
     "start_time": "2025-04-11T12:48:50.382239Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='YlGnBu',\n",
    "            xticklabels=mlp.classes_, yticklabels=mlp.classes_)\n",
    "plt.title('MLP Classifier Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ],
   "id": "c882cb1c18d66c7d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T12:51:29.672143Z",
     "start_time": "2025-04-11T12:48:52.173350Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "import time\n",
    "\n",
    "# 数据预处理\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.1307,), (0.3081,))\n",
    "])\n",
    "\n",
    "# 加载训练集和测试集\n",
    "train_dataset = datasets.MNIST(root='./data', train=True,\n",
    "                               download=True, transform=transform)\n",
    "test_dataset = datasets.MNIST(root='./data', train=False,\n",
    "                              download=True, transform=transform)\n",
    "\n",
    "# 创建数据加载器\n",
    "trainloader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "testloader = DataLoader(test_dataset, batch_size=64, shuffle=False)\n",
    "\n",
    "# 定义简单的 CNN 模型\n",
    "class SimpleCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)\n",
    "        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)\n",
    "        self.fc1 = nn.Linear(32 * 7 * 7, 128)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(self.relu(self.conv1(x)))\n",
    "        x = self.pool(self.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 32 * 7 * 7)\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# 初始化模型、损失函数和优化器\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = SimpleCNN().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "# 训练模型\n",
    "start_time = time.time()\n",
    "for epoch in range(10):  # 训练 10 个 epoch\n",
    "    model.train()\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "    print(f\"Epoch {epoch + 1}, Loss: {running_loss / len(trainloader)}\")\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 评估模型\n",
    "model.eval()\n",
    "correct = 0\n",
    "total = 0\n",
    "y_pred = []\n",
    "y_true = []\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        inputs, labels = data\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        outputs = model(inputs)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        y_pred.extend(predicted.cpu().numpy())\n",
    "        y_true.extend(labels.cpu().numpy())\n",
    "\n",
    "accuracy = accuracy_score(y_true, y_pred)\n",
    "precision = precision_score(y_true, y_pred, average='weighted')\n",
    "recall = recall_score(y_true, y_pred, average='weighted')\n",
    "f1 = f1_score(y_true, y_pred, average='weighted')\n",
    "conf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "print(\"CNN - Accuracy:\", accuracy)\n",
    "print(\"CNN - Precision:\", precision)\n",
    "print(\"CNN - Recall:\", recall)\n",
    "print(\"CNN - F1 Score:\", f1)\n",
    "print(\"CNN - Confusion Matrix:\\n\", conf_matrix)\n",
    "print(\"CNN - Training Time:\", train_time, \"seconds\")"
   ],
   "id": "238c29b8cbcb06f3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1, Loss: 0.16785865077482803\n",
      "Epoch 2, Loss: 0.048593001983894\n",
      "Epoch 3, Loss: 0.03351195106263097\n",
      "Epoch 4, Loss: 0.025130573074459466\n",
      "Epoch 5, Loss: 0.01867187536363747\n",
      "Epoch 6, Loss: 0.014864017328177678\n",
      "Epoch 7, Loss: 0.011946242721634592\n",
      "Epoch 8, Loss: 0.010253890976666399\n",
      "Epoch 9, Loss: 0.008302276589712516\n",
      "Epoch 10, Loss: 0.0076927815408115744\n",
      "CNN - Accuracy: 0.9903\n",
      "CNN - Precision: 0.9903519211758518\n",
      "CNN - Recall: 0.9903\n",
      "CNN - F1 Score: 0.990303968175733\n",
      "CNN - Confusion Matrix:\n",
      " [[ 976    0    0    0    0    1    0    1    2    0]\n",
      " [   2 1128    0    1    1    1    0    2    0    0]\n",
      " [   2    1 1021    0    1    0    0    4    3    0]\n",
      " [   1    0    2  997    0    5    0    1    4    0]\n",
      " [   1    0    0    0  967    0    0    1    2   11]\n",
      " [   1    0    0    2    0  888    1    0    0    0]\n",
      " [   6    2    1    0    2    5  940    0    2    0]\n",
      " [   0    0    4    0    0    0    0 1020    1    3]\n",
      " [   0    0    1    1    0    2    0    1  965    4]\n",
      " [   0    0    0    0    2    1    0    2    3 1001]]\n",
      "CNN - Training Time: 149.85204672813416 seconds\n"
     ]
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-04-11T12:51:33.179338Z",
     "start_time": "2025-04-11T12:51:32.634814Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 绘制混淆矩阵热力图\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='YlGnBu')\n",
    "plt.title('CNN Confusion Matrix')\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "plt.show()"
   ],
   "id": "187b20a42c4e8eb7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "5d7eaf04c52f1648"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
